Another important extension of the aggregation technique is the stability change-point detection. Such a stability selection introduced by ([1]}) was designed to improve the performance of variable selection and provide control for false discoveries. We demonstrate that the idea of aggregating results by applying a procedure to subsamples of the data can be well implemented under our framework. One can aggregate the estimation from the Bernoulli sequences, and select the estimated change-point locations with votes beyond a predetermined threshold. To our limited knowledge, this could be the first method in the change-point literature that holds both asymptotic property and finite-sample control of false discoveries.
